Gemini AI MCP SERVER

🚀 Gemini AI MCP Server

The Gemini AI MCP Server integrates Google’s Gemini language models with Apify’s automation platform using the Model Context Protocol (MCP). This actor acts as a bridge between scraping workflows and AI analysis, allowing real-time natural-language understanding, data summarization, classification, and structured extraction within your Apify or AI pipelines.

🧠 Key Features

✅ Gemini API integration Seamlessly connects with Google’s Gemini models (gemini-2.5-flash, etc.) using your API key.

✅ Real-time AI data processing Analyze and interpret scraped datasets as soon as they’re collected — directly in your Apify workflows.

✅ Flexible prompt customization Define tasks such as summarization, sentiment analysis, categorization, or structured extraction using natural language.

✅ Apify workflow integration Run any Apify Actor (e.g., bhansalisoft~google-map-business-scraper) and automatically analyze its dataset output using Gemini AI.

✅ Secure key storage Users can safely add, update, or verify their Gemini API key using interactive MCP tools.

✅ Structured output for automation Return JSON-formatted results suitable for further processing or database insertion.

🧩 Tools Exposed by MCP Server

Tool name	Description
save_gemini_api_key(api_key)	Saves the user-provided Gemini API key locally for persistent sessions.
gemini_status	Checks if the saved API key is valid and the model responds correctly.
scrape_and_analyze(apify_actor_id, input_json, task_prompt)	Runs an Apify scraper actor, fetches dataset results, and analyzes them via Gemini AI.
gemini_analyze_text(task, text)	Analyze raw text with Gemini (summarization, sentiment, etc.).
gemini_analyze_url(url, task)	Fetches a web page, extracts text, and performs Gemini analysis.
gemini_categorize(labels_json, text)	Categorizes text into provided labels and returns label + confidence.
gemini_structured_extract(text, schema_json)	Extracts structured data based on a given JSON schema.
gemini_embed_text(text)	Returns an embedding vector for semantic search or clustering.
gemini_set_defaults(model, temperature)	Updates the default Gemini model and temperature used by the server.
summarize_scraped_data(json_data)	Summarizes pre-scraped JSON data using Gemini AI.

🔍 MCP Connection Configuration (Claude Desktop / LangGraph)

MCP SERVER URL Will be

https://bhansalisoft--gemini-mcp-server.apify.actor/mcp

Add to your claude_desktop_config.json:

{
    "mcpServers": {
        "gemini-mcp-server": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://bhansalisoft--gemini-mcp-server.apify.actor/mcp?token=[Your APIFY KEY]"
            ]
        }
    }
}

After connection, all Gemini tools will appear automatically in your MCP client.

Demo Videos

Check Demo video using TEST MCP Client

Check Demo using MCP inspector Tools